Data QA Engineer

We are seeking an experienced Data QA Engineer to ensure data reliability and accuracy across the data lifecycle.

This role focuses on testing data pipelines, ETL processes, and data warehouses to guarantee completeness, consistency, and validity for business use.

The ideal candidate will build automated test frameworks, define data quality rules, and collaborate with data engineers and analysts to maintain high data integrity.

Key Responsibilities

• Testing & Validation: Design and execute tests for data ingestion, transformation (ETL/ELT), and storage in data warehouses/lakes.
• Data Profiling & Monitoring: Analyze data structures, identify anomalies, and set up continuous monitoring for quality issues.
• Automation: Build automated test frameworks using Python, Pytest, and SQL to detect defects early.
• Rule Definition: Create and implement data quality rules and metrics (e.g., completeness, accuracy).
• Collaboration: Work closely with data engineers, analysts, and business stakeholders to understand requirements and resolve defects.

Required Qualifications

• Experience: 6–10+ years in data QA, data engineering, or related roles.
• Technical Skills:
  • Strong proficiency in SQL (complex queries).
  • Expertise in Python (Pandas, PySpark).
  • Hands-on experience with data warehousing and cloud platforms (AWS, Azure, GCP).
  • Familiarity with BI tools such as Tableau or Power BI.
• Analytical Skills: Strong problem-solving ability, attention to detail, and understanding of data modeling, lifecycle, and governance.
• Methodologies: Knowledge of data testing processes, data governance, and CI/CD practices.

Preferred Qualifications

• Experience with data quality frameworks and advanced automation strategies.
• Exposure to big data ecosystems and distributed computing environments.